Detecting Unusual Activity in Video

10 years 7 months ago
Detecting Unusual Activity in Video
We present an unsupervised technique for detecting unusual activity in a large video set using many simple features. No complex activity models and no supervised feature selections are used. We divide the video into equal length segments and classify the extracted features into prototypes, from which a prototype?segment co-occurrence matrix is computed. Motivated by a similar problem in documentkeyword analysis, we seek a correspondence relationship between prototypes and video segments which satisfies the transitive closure constraint. We show that an important sub-family of correspondence functions can be reduced to co-embedding prototypes and segments to N-D Euclidean space. We prove that an efficient, globally optimal algorithm exists for the co-embedding problem. Experiments on various real-life videos have validated our approach.
Hua Zhong, Jianbo Shi, Mirkó Visontai
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2004
Where CVPR
Authors Hua Zhong, Jianbo Shi, Mirkó Visontai
Comments (0)